Automated assessment of Chinese subjective questions is a crossed research direction on linguistics, natural language processing (NLP) and related disciplines. In this paper, we focus on correcting political subjective questions, and based on the analysis of the manual scoring process, a novel automatic scoring framework is created. It mainly includes two parts. Firstly, we represent the sentence semantic by an unsupervised model that involves a weighted average of the word vectors. Then, we propose a correction algorithm which combines keywords matching and semantic similarity computation. Comparison between the results made by our framework and the teacher proves reasonableness of the model.